Data Science (Gift City) MSc - 2026/7
Awarding body
University of Surrey
Teaching institute
University of Surrey
Framework
FHEQ Level 7
Final award and programme/pathway title
MSc Data Science (Gift City)
Subsidiary award(s)
| Award | Title |
|---|---|
| PGDip | Data Science (Gift City) |
| PGCert | Data Science (Gift City) |
Modes of study
| Route code | Credits and ECTS Credits | |
| Full-time | PKA61168 | 180 credits and 90 ECTS credits |
QAA Subject benchmark statement (if applicable)
Other internal and / or external reference points
This programme is subject to approval. It is going to be delivered in Gift City (IBC). This means that it has received initial agreement from the University and is currently undergoing a detailed final approval exercise, through the University's quality assurance processes. These processes are a requirement for all Higher Education Institutions within the UK, to ensure that programmes are of the highest standard. Occasionally there may be instances where the University may delay or not approve the introduction of the programme.
Faculty and Department / School
Faculty of Engineering and Physical Sciences - Computer Science and Electronic Eng
Programme Leader
THORNE Tom (CS & EE)
Date of production/revision of spec
19/11/2025
Educational aims of the programme
- The key educational aim of the programme is to prepare students for a variety of leading roles in data science. Such roles will involve data-intensive computing and lead to positions as data scientists, data analysts, data engineers and data architects, as well as business analysts and database administrators, with expected progression through to managerial roles involving teams of these. Creation, collection, management and analysis of data is core to a wide range of industry activities, from politics to advertising, health, finance, and numerous others.
Programme learning outcomes
| Attributes Developed | Awards | Ref. | |
| The principles and practices of data science | K | PGCert, PGDip, MSc | |
| The principles and applications of data science technologies | K | PGCert, PGDip, MSc | |
| The professional issues involved in the exploitation of data | K | PGCert, PGDip, MSc | |
| The areas of emergent and innovative data science technologies | K | PGCert, PGDip, MSc | |
| The key research issues in data science | K | PGCert, PGDip, MSc | |
| Understand, articulate, and demonstrate how to achieve the requirements of the users of data science applications | C | PGCert, PGDip, MSc | |
| Research, develop, and evaluate data science methods | C | PGCert, PGDip, MSc | |
| Specify, design and develop solutions to complex and substantial data science problems | C | PGCert, PGDip, MSc | |
| The practices and business relevance of data science | P | PGCert, PGDip, MSc | |
| The ability to critically evaluate software systems and tools | P | PGCert, PGDip, MSc | |
| The capability to work as an effective member of a team | P | PGCert, PGDip, MSc | |
| The ability to communicate effectively with specialists and non-specialists to understand their needs | P | PGCert, PGDip, MSc | |
| The ability to apply and justify appropriate ways to analyse data and present information | P | PGCert, PGDip, MSc | |
| The ability to plan, research, manage and implement a major project | P | PGCert, PGDip, MSc | |
| Research and information retrieval skills | T | PGCert, PGDip, MSc | |
| Numeracy in both understanding and presenting cases involving a quantitative dimension | T | PGCert, PGDip, MSc | |
| Self-learning skills | T | PGCert, PGDip, MSc | |
| Succinctly present, to a range of audiences, knowledge relevant to the building, testing and deployment of a system | T | PGCert, PGDip, MSc | |
| Time management and organisational skills | T | PGCert, PGDip, MSc | |
| Effective use of specialist IT facilities | T | PGCert, PGDip, MSc | |
| Continuing professional development | T | PGCert, PGDip, MSc |
Attributes Developed
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Programme structure
Full-time
This Master's Degree programme is studied full-time over one academic year, consisting of 180 credits at FHEQ level 7. All modules are semester based and worth 15 credits with the exception of project, practice based and dissertation modules.
Possible exit awards include:
- Postgraduate Diploma (120 credits)
- Postgraduate Certificate (60 credits)
Programme Adjustments (if applicable)
N/A
Modules
Year 1 (full-time) - FHEQ Level 7
Opportunities for placements / work related learning / collaborative activity
| Associate Tutor(s) / Guest Speakers / Visiting Academics | Y | |
| Professional Training Year (PTY) | N | |
| Placement(s) (study or work that are not part of PTY) | N | |
| Clinical Placement(s) (that are not part of the PTY scheme) | N | |
| Study exchange (Level 5) | N | |
| Dual degree | N |
Other information
The school/department of Computer Science and Electronic Engineering is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability, and Resourcefulness and Resilience. This programme is designed to allow students to develop knowledge, skills, and capabilities in the following areas:
Digital capabilities: Strong technical skills are critical to being a data scientist and this programme provides a solid technical grounding in the practical side of data science. Modules such as Business Analytics with Data Visualisation or Machine Learning for Data Science give students experience solving technical problems with industry standard languages such as Python and R and using real world data sets wherever possible. In the Master Dissertation module, students get the opportunity to design and develop a technical solution to a problem of their choice. Digital skills are key for many industry jobs and this programme aims to develop both the foundational underpinning as well as industry ready digital skills.
Employability: This Data Science programme provides the foundational theory and practical skills that allow our students to work in a range of different industries such as tech, or finance. Wherever possible we use industry standard languages such as Python and R to provide students with the practical skills that will allow them to compete for technical data science and AI jobs. On top of this, we ensure students have an understanding of the fundamentals of data science as this will allow them to apply their knowledge to new technologies and new situations. Where possible, we work with real world problems and modules such as Business Analytics with Data Visualisation will allow students to work together to solve a large scale problem in a situation similar to what would be expected of them in an industry context. This programme offers a placement option which gives students the benefit of a year working in industry to improve their employment prospects.
Global and cultural capabilities: Computer Science is a global language and the tools and languages used on this programme can be used internationally. Students learn work together in groups with other students from different backgrounds to solve a problem. These programme allows students to develop skills that will allow them to build applications with global reach and collaborate with their peers around the world.
Resourcefulness and Resilience: This programme requires practical problem-solving skills that teach a student how to reason about and solve new unseen problems starting with a problem scenario and designing and developing a complex and practical solution to the problem. A typical coursework will present a scenario with a data set (often in real world context) and ask students to develop a solution to processing and analysing the data. This requires not just technical development skills but the planning and problems-solving skill to approach a large problem, break it down into smaller chunks and solve and integrate these chunks into a working solution. We encourage an open ended nature to our practical work where possible. This encourages students to go beyond the taught material and deliver innovative solutions to large scale problems. A module such as Machine Learning for Data Science teaches students how to work in group to plan and execute a complex project. The MSc Dissertation module requires student to use these skills to take an idea concept through to implementation and write a professional report detailing their work.
Sustainability: Computers are embedded within almost every industry including industries such as energy and agriculture to enhance sustainability. As part of the MSc Dissertation module, students have the opportunity to work in many areas including supporting the UN Sustainability goals.
Quality assurance
The Regulations and Codes of Practice for taught programmes can be found at:
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2026/7 academic year.